journal article Sep 01, 2024

Radiomics-based prediction of recurrence for head and neck cancer patients using data imbalanced correction

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References
49
[1]
Head and neck squamous cell carcinoma

Daniel E. Johnson, Barbara Burtness, C. René Leemans et al.

Nature Reviews Disease Primers 2020 10.1038/s41572-020-00224-3
[2]
Ferlay "Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods" Int. J. Cancer (2019) 10.1002/ijc.31937
[3]
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Freddie Bray, Jacques Ferlay, Isabelle Soerjomataram et al.

CA: A Cancer Journal for Clinicians 2018 10.3322/caac.21492
[4]
Jean "Hyperfractionated or accelerated radiotherapy in head and neck cancer: a meta-analysis" Lancet (2006) 10.1016/s0140-6736(06)69121-6
[5]
Fausto Petrelli "Comparison of different treatments for HPV+oropharyngeal carcinoma: a network meta-analysis" Eur. Arch. Oto-Rhino-Laryngol. (2023) 10.1007/s00405-022-07710-2
[6]
Du "Long-term survival in head and neck cancer: impact of site, stage, smoking, and human papillomavirus status" Laryngoscope (2019) 10.1002/lary.27807
[7]
Machine learning: Trends, perspectives, and prospects

M. I. Jordan, T. M. Mitchell

Science 2015 10.1126/science.aaa8415
[8]
Rahul "Deo. Machine learning in medicine" Circulation (2015) 10.1161/circulationaha.115.001593
[9]
Radiomics: the process and the challenges

Virendra Kumar, Yuhua Gu, Satrajit Basu et al.

Magnetic Resonance Imaging 2012 10.1016/j.mri.2012.06.010
[10]
Radiomics: Extracting more information from medical images using advanced feature analysis

Philippe Lambin, Emmanuel Rios-Velazquez, Ralph Leijenaar et al.

European Journal of Cancer 2012 10.1016/j.ejca.2011.11.036
[11]
Ning "Application of CT radiomics in prediction of early recurrence in hepatocellular carcinoma" Abdominal Radiology (2020) 10.1007/s00261-019-02198-7
[12]
The class imbalance problem: A systematic study1

Nathalie Japkowicz, Shaju Stephen

Intelligent Data Analysis 2002 10.3233/ida-2002-6504
[13]
Elkan "The foundations of cost-sensitive learning" (2001)
[14]
Anomaly detection

Varun Chandola, Arindam Banerjee, Vipin Kumar

ACM Computing Surveys 2009 10.1145/1541880.1541882
[15]
SMOTE: Synthetic Minority Over-sampling Technique

N. V. Chawla, K. W. Bowyer, L. O. Hall et al.

Journal of Artificial Intelligence Research 2002 10.1613/jair.953
[16]
Training and assessing classification rules with imbalanced data

Giovanna Menardi, Nicola Torelli

Data Mining and Knowledge Discovery 2014 10.1007/s10618-012-0295-5
[17]
Borderline over-sampling for imbalanced data classification

Hien M. Nguyen, Eric W. Cooper, Katsuari Kamei

International Journal of Knowledge Engineering and... 2011 10.1504/ijkesdp.2011.039875
[18]
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE

Georgios Douzas, Fernando Bacao, Felix Last

Information Sciences 2018 10.1016/j.ins.2018.06.056
[19]
Han "Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning" (2005)
[20]
He "Adaptive synthetic sampling approach for imbalanced learning" (2008)
[21]
Prusty "Outlier-SMOTE: a refined oversampling technique for improved detection of COVID-19" Intelligence-Based Medicine (2020)
[22]
Jiang "A hybrid method to predict postoperative survival of lung cancer using improved SMOTE and adaptive SVM" Comput. Math. Methods Med. (2021)
[23]
Alhudhaif "A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach" PeerJ Computer Science (2021) 10.7717/peerj-cs.523
[24]
Beinecke "Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making" BioData Min. (2021) 10.1186/s13040-021-00283-6
[25]
Kawahara "Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis" Sci. Rep. (2021) 10.1038/s41598-021-95643-x
[26]
Hongxia "Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer" Phys. Med. Biol. (2018)
[27]
Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy

Congying Xie, Pengfei Yang, Xuebang Zhang et al.

EBioMedicine 2019 10.1016/j.ebiom.2019.05.023
[28]
Tang "Radiomics AI prediction for head and neck squamous cell carcinoma (HNSCC) prognosis and recurrence with target volume approach" BJR—Open (2021)
[29]
Wee (2019)
[30]
Aerts "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach" Nat. Commun. (2014) 10.1038/ncomms5006
[31]
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Kenneth Clark, Bruce Vendt, Kirk Smith et al.

Journal of Digital Imaging 2013 10.1007/s10278-013-9622-7
[32]
Grossberg (2017)
[33]
Grossberg "Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy" Sci. Data (2018)
[34]
Regression Shrinkage and Selection via The Lasso: A Retrospective

Robert Tibshirani

Journal of the Royal Statistical Society Series B:... 2011 10.1111/j.1467-9868.2011.00771.x
[35]
Peterson "K-nearest neighbor" Scholarpedia (2009) 10.4249/scholarpedia.1883
[36]
Support vector machines

M.A. Hearst, S.T. Dumais, E. Osuna et al.

IEEE Intelligent Systems and their Applications 1998 10.1109/5254.708428
[37]
Zhang "Predicting local persistence/recurrence after radiation therapy for head and neck cancer from PET/CT using a multi-objective, multi-classifier radiomics model" Front. Oncol. (2022)
[38]
Kawahara "Radiomic analysis for pretreatment prediction of recurrence post-radiotherapy in cervical squamous cell carcinoma cancer" Diagnostics (2022) 10.3390/diagnostics12102346
[39]
Yang "Intratumor heterogeneity predicts metastasis of triple-negative breast cancer" Carcinogenesis (2017) 10.1093/carcin/bgx071
[40]
Burrell "The causes and consequences of genetic heterogeneity in cancer evolution" Nature (2013) 10.1038/nature12625
[41]
Liu "The significance of intertumor and intratumor heterogeneity in liver cancer" Exp. Mol. Med. (2018) 10.1038/emm.2017.165
[42]
Morris "Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival" Oncotarget (2016) 10.18632/oncotarget.7067
[43]
Choi "Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma" Oncotarget (2016) 10.18632/oncotarget.11693
[44]
Hwan Moon "Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer" Eur. J. Nucl. Med. Mol. Imag. (2019) 10.1007/s00259-018-4138-5
[45]
Tian "Radiomics-based machine-learning method for prediction of distant metastasis from soft-tissue sarcomas" Clin. Radiol. (2021) 10.1016/j.crad.2020.08.038
[46]
Wang "MRI-Based pre-radiomics and delta-radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy" Front. Oncol. (2023)
[47]
Zhang "Radiomics-based prognosis analysis for non-small cell lung cancer" Sci. Rep. (2017)
[48]
Brandt (2021)
[49]
Xie "Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PETbased radiomics model on prognostication performance in cohorts of head and neck cancer patients" Eur. J. Nucl. Med. Mol. Imag. (2020) 10.1007/s00259-020-04756-4
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Published
Sep 01, 2024
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Hiroki Oka, Daisuke Kawahara, Yuji Murakami (2024). Radiomics-based prediction of recurrence for head and neck cancer patients using data imbalanced correction. Computers in Biology and Medicine, 180, 108879. https://doi.org/10.1016/j.compbiomed.2024.108879